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Utilizing machine learning, this project employs emotion-driven models to recommend movies. By analyzing user reactions, it tailors suggestions for an emotionally engaging cinematic experience.

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ronobrian-eng/movie-recommendation-based-on-emotion

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Movie Recommendation Based on Emotion

Introduction: Welcome to the Movie Recommendation Based on Emotion project! This project utilizes machine learning to recommend movies tailored to the user's emotions. By analyzing emotional cues and preferences, the model suggests movies that align with the user's current mood, creating a personalized and enjoyable movie-watching experience.

Features:

  • Emotion-based movie recommendations
  • User-friendly interface for inputting emotions
  • Integration with popular movie databases

Usage: Prerequisites: Before getting started, make sure you have the following dependencies installed:

  • Python 3.x
  • Required Python packages: NumPy, pandas, scikit-learn
  • API access for movie databases (e.g., IMDb, TMDb)

Installation:

  1. Clone the repository:
    git clone https://github.com/YourUsername/movie-recommendation-emotion.git
    cd movie-recommendation-emotion
    

Set up a virtual environment (optional but recommended): python -m venv venv source venv/bin/activate # On Windows: .\venv\Scripts\activate

Install dependencies: pip install -r requirements.txt

Obtain API keys: Sign up for API access on movie databases (e.g., IMDb, TMDb) Add API keys to the configuration file (config.json) Running the Project:

Configure API keys: Open config.json and add your API keys.

Execute the project: python main.py Input your emotions when prompted and receive personalized movie recommendations.

License: This project is licensed under the MIT License.

Contributing: Feel free to contribute by opening issues or submitting pull requests. Your feedback and suggestions are highly appreciated!

Acknowledgments: Thanks to the creators of movie databases for providing valuable APIs. Enjoy your movie recommendations based on emotion!

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Utilizing machine learning, this project employs emotion-driven models to recommend movies. By analyzing user reactions, it tailors suggestions for an emotionally engaging cinematic experience.

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